In [1]:
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
from tensorflow.keras import optimizers
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import Dropout
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.layers import Flatten
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
!pip install yfinance 
import yfinance as yf

Requirement already satisfied: yfinance in c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages (0.2.31)
Requirement already satisfied: pandas>=1.3.0 in c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages (from yfinance) (1.4.2)
Requirement already satisfied: numpy>=1.16.5 in c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages (from yfinance) (1.22.4)
Requirement already satisfied: requests>=2.31 in c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages (from yfinance) (2.31.0)
Requirement already satisfied: multitasking>=0.0.7 in c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages (from yfinance) (0.0.11)
Requirement already satisfied: lxml>=4.9.1 in c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages (from yfinance) (4.9.3)
Requirement already satisfied: appdirs>=1.4.4 in c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages (from yfinance) (1.4.4)
Requirement already satisfied: pytz>=2022.5 in c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages (from yfinance) (2023.3.post1)
Requirement already satisfied: frozendict>=2.3.4 in c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages (from yfinance) (2.3.8)
Requirement already satisfied: peewee>=3.16.2 in c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages (from yfinance) (3.16.3)
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Requirement already satisfied: urllib3<3,>=1.21.1 in c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages (from requests>=2.31->yfinance) (1.26.9)
Requirement already satisfied: certifi>=2017.4.17 in c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages (from requests>=2.31->yfinance) (2021.10.8)

WARNING: Ignoring invalid distribution -orch (c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages)
WARNING: Ignoring invalid distribution -pencv-python (c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages)
WARNING: Ignoring invalid distribution -orch (c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages)
WARNING: Ignoring invalid distribution -pencv-python (c:\users\yesub\anaconda3\new folder\anaconda\lib\site-packages)

[notice] A new release of pip is available: 23.3.2 -> 24.0
[notice] To update, run: python.exe -m pip install --upgrade pip
In [2]:
df= yf.Ticker("^NSEBANK").history(period='3y').reset_index()
df_axis= yf.Ticker("AXISBANK.NS").history(period='3y').reset_index()
df_sbi= yf.Ticker("SBIN.NS").history(period='3y').reset_index()
df_hdfc= yf.Ticker("HDFC.NS").history(period='3y').reset_index()
df_pnb= yf.Ticker("PNB.NS").history(period='3y').reset_index()
df_kot= yf.Ticker("KOTAKBANK.NS").history(period='3y').reset_index()
df_icic= yf.Ticker("ICICIBANK.NS").history(period='3y').reset_index()

df_rbl= yf.Ticker("RBLBANK.NS").history(period='3y').reset_index()
df_ind= yf.Ticker("INDUSINDBK.NS").history(period='3y').reset_index()
df_idfc= yf.Ticker("IDFCFIRSTB.NS").history(period='3y').reset_index()
df_band= yf.Ticker("BANDHANBNK.NS").history(period='3y').reset_index()
df_fed= yf.Ticker("FEDERALBNK.NS").history(period='3y').reset_index()
df_au= yf.Ticker("AUBANK.NS").history(period='3y').reset_index()
In [3]:
df.head()
Out[3]:
Date Open High Low Close Volume Dividends Stock Splits
0 2021-05-28 00:00:00+05:30 35345.235898 35436.234836 34976.790980 35141.039062 0 0.0 0.0
1 2021-05-31 00:00:00+05:30 35097.040159 35583.886047 34929.694453 35526.234375 0 0.0 0.0
2 2021-06-01 00:00:00+05:30 35639.336332 35713.483909 35240.790177 35336.789062 0 0.0 0.0
3 2021-06-02 00:00:00+05:30 35271.192591 35415.440918 35069.894143 35373.339844 0 0.0 0.0
4 2021-06-03 00:00:00+05:30 35536.036464 35723.284289 35412.588679 35648.585938 0 0.0 0.0
In [4]:
import plotly.express as px
fig = px.line(df, x='Date', y="Open")
fig.show()
In [5]:
print(df_axis.Date.max())
print(df_axis.Date.min())

2024-05-28 00:00:00+05:30
2021-05-28 00:00:00+05:30
In [6]:
fig, axes = plt.subplots(6, 2, sharex=True, figsize=(20,32))
plt.grid(True)
sns.lineplot(ax=axes[0, 0], data=df_axis, x='Date', y='Open')
axes[0,0].set_title('axis')
sns.lineplot(ax=axes[0, 1], data=df_sbi, x='Date', y='Open')
axes[0,1].set_title('sbi')
sns.lineplot(ax=axes[1, 0], data=df_rbl, x='Date', y='Open')
axes[1,0].set_title('rbl')
sns.lineplot(ax=axes[1, 1], data=df_pnb, x='Date', y='Open')
axes[1,1].set_title('pnb')
sns.lineplot(ax=axes[2, 0], data=df_kot, x='Date', y='Open')
axes[2,0].set_title('kot')
sns.lineplot(ax=axes[2, 1], data=df_ind, x='Date', y='Open')
axes[2,1].set_title('ind')
sns.lineplot(ax=axes[3, 0], data=df_idfc, x='Date', y='Open')
axes[3,0].set_title('idfc')
sns.lineplot(ax=axes[3, 1], data=df_icic, x='Date', y='Open')
axes[3,1].set_title('icic')
sns.lineplot(ax=axes[4, 0], data=df_band, x='Date', y='Open')
axes[4,0].set_title('band')
sns.lineplot(ax=axes[2, 1], data=df_hdfc, x='Date', y='Open')
axes[4,1].set_title('hdfc')
sns.lineplot(ax=axes[5, 0], data=df_fed, x='Date', y='Open')
axes[5,0].set_title('fed')
sns.lineplot(ax=axes[5,1], data=df_au, x='Date', y='Open')
axes[5,1].set_title('au')
Out[6]:
Text(0.5, 1.0, 'au')
In [7]:
print(df.shape)
date_train=pd.to_datetime(df['Date'])
date_train

(738, 8)
Out[7]:
0     2021-05-28 00:00:00+05:30
1     2021-05-31 00:00:00+05:30
2     2021-06-01 00:00:00+05:30
3     2021-06-02 00:00:00+05:30
4     2021-06-03 00:00:00+05:30
                 ...           
733   2024-05-22 00:00:00+05:30
734   2024-05-23 00:00:00+05:30
735   2024-05-24 00:00:00+05:30
736   2024-05-27 00:00:00+05:30
737   2024-05-28 00:00:00+05:30
Name: Date, Length: 738, dtype: datetime64[ns, Asia/Kolkata]
In [8]:
Scale=StandardScaler()
def data_prep(df, lookback, future, Scale):
    date_train=pd.to_datetime(df['Date'])
    df_train=df[['Open','High','Low','Close','Volume','Dividends','Stock Splits']]
    df_train=df_train.astype(float)
    
    df_train_scaled=Scale.fit_transform(df_train)

    X, y =[],[]
    for i in range(lookback, len(df_train_scaled)-future+1):
        X.append(df_train_scaled[i-lookback:i, 0:df_train.shape[1]])
        y.append(df_train_scaled[i+future-1:i+future, 0])
        
    return np.array(X), np.array(y), df_train, date_train
Lstm_x, Lstm_y, df_train, date_train = data_prep(df, 30, 1, Scale)
In [9]:
def Lstm_fallback(X,y):
    model = Sequential()
    
    model.add(LSTM(64, activation='relu',input_shape=(X.shape[1], X.shape[2]),  return_sequences=True))
    model.add(Dropout(0.2))
    model.add(BatchNormalization())
    model.add(LSTM(32, activation='relu', return_sequences=False))
    model.add(Dropout(0.2))
    model.add(BatchNormalization())
    model.add(Dense(32, activation='relu'))
    model.add(Dense(y.shape[1], activation='relu'))
    
    opt = tf.keras.optimizers.Adam(lr=0.001, decay=1e-6)  
    model.compile(
            loss='mse',
            optimizer=opt,
        )
    
    es = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=15, restore_best_weights=True)
    model.fit(X, y, epochs=100, verbose=1, callbacks=[es], validation_split=0.1, batch_size=16)
    return model
In [10]:
def Lstm_model1(X, y):
    regressor = Sequential()

    regressor.add(LSTM(units = 50, return_sequences = True, input_shape = (X.shape[1], X.shape[2])))
    regressor.add(Dropout(0.2))
    regressor.add(LSTM(units = 50, return_sequences = True))
    regressor.add(Dropout(0.2))
    regressor.add(LSTM(units = 50, return_sequences = True))
    regressor.add(Dropout(0.2))
    regressor.add(LSTM(units = 50))
    regressor.add(Dropout(0.2))
    regressor.add(Dense(units = 1))

    regressor.compile(optimizer = 'adam', loss = 'mean_squared_error')
    
    es = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=15, restore_best_weights=True)
    regressor.fit(X, y, epochs = 100, validation_split=0.1, batch_size = 64, verbose=1, callbacks=[es])
    return regressor
In [11]:
def Lstm_model2(X,y):
    model=Sequential()
    
    model.add(LSTM(20,return_sequences=True,input_shape=(X.shape[1], X.shape[2])))
    model.add(Dropout(0.2))
    model.add(BatchNormalization())
    #model.add(LSTM(15,return_sequences=True))
    #model.add(Dropout(0.2))
    #model.add(BatchNormalization())
    model.add(LSTM(15))
    model.add(Dropout(0.2))
    model.add(BatchNormalization())
    model.add(Dense(16, activation='relu'))
    model.add(Dense(1))
    
    adam = optimizers.Adam(0.001)
    model.compile(loss='mean_squared_error',optimizer=adam)
    
    es = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=15, restore_best_weights=True)
    model.fit(X, y,validation_split=0.2,epochs=100,batch_size=64,verbose=1, callbacks=[es])
    return model
In [12]:
def predict_open(model,date_train,Lstm_x,df_train, future, Scale):
    forecasting_dates=pd.date_range(list(date_train)[-1], periods=future, freq='1d').tolist()
    predicted=model.predict(Lstm_x[-future:])
    predicted1=np.repeat(predicted, df_train.shape[1], axis=-1)
    predicted_descaled=Scale.inverse_transform(predicted1)[:,0]
    return predicted_descaled,forecasting_dates

def output_prep(forecasting_dates,predicted_descaled):
    dates=[]
    for i in forecasting_dates:
        dates.append(i.date())
    df_final=pd.DataFrame(columns=['Date','Open'])
    df_final['Date']=pd.to_datetime(dates)
    df_final['Open']=predicted_descaled
    return df_final
In [13]:
def results(df, lookback, future, Scale, x):
    Lstm_x, Lstm_y, df_train, date_train = data_prep(df, lookback, future, Scale)
    model=Lstm_model1(Lstm_x,Lstm_y)
    loss=pd.DataFrame(model.history.history)
    loss.plot()
    future=30
    predicted_descaled,forecasting_dates=predict_open(model,date_train,Lstm_x,df_train,future, Scale)
    results=output_prep(forecasting_dates,predicted_descaled)   
    print(results.head())
    plt.show()
    fig = px.area(results, x="Date", y="Open", title=x)
    fig.update_yaxes(range=[results.Open.min()-10, results.Open.max()+10])
    fig.show()
In [14]:
def results1(df, lookback, future, Scale, x):
    Lstm_x, Lstm_y, df_train, date_train = data_prep(df, lookback, future, Scale)
    model=Lstm_model2(Lstm_x,Lstm_y)
    loss=pd.DataFrame(model.history.history)
    loss.plot()
    future=30
    predicted_descaled,forecasting_dates=predict_open(model,date_train,Lstm_x,df_train,future, Scale)
    results=output_prep(forecasting_dates,predicted_descaled)   
    print(results.head())
    plt.show()
    fig = px.area(results, x="Date", y="Open", title=x)
    fig.update_yaxes(range=[results.Open.min()-10, results.Open.max()+10])
    fig.show()
In [15]:
results(df, 30, 1, Scale, 'NSEBANK')

Epoch 1/100
10/10 [==============================] - 25s 690ms/step - loss: 0.3557 - val_loss: 0.0974
Epoch 2/100
10/10 [==============================] - 2s 188ms/step - loss: 0.1509 - val_loss: 0.4312
Epoch 3/100
10/10 [==============================] - 2s 173ms/step - loss: 0.1186 - val_loss: 0.1838
Epoch 4/100
10/10 [==============================] - 1s 147ms/step - loss: 0.1037 - val_loss: 0.1278
Epoch 5/100
10/10 [==============================] - 1s 149ms/step - loss: 0.0936 - val_loss: 0.0474
Epoch 6/100
10/10 [==============================] - 2s 174ms/step - loss: 0.0808 - val_loss: 0.0591
Epoch 7/100
10/10 [==============================] - 2s 184ms/step - loss: 0.0794 - val_loss: 0.1182
Epoch 8/100
10/10 [==============================] - 2s 182ms/step - loss: 0.0753 - val_loss: 0.0861
Epoch 9/100
10/10 [==============================] - 2s 182ms/step - loss: 0.0727 - val_loss: 0.0848
Epoch 10/100
10/10 [==============================] - 2s 182ms/step - loss: 0.0700 - val_loss: 0.0918
Epoch 11/100
10/10 [==============================] - 2s 185ms/step - loss: 0.0653 - val_loss: 0.0909
Epoch 12/100
10/10 [==============================] - 2s 184ms/step - loss: 0.0638 - val_loss: 0.1123
Epoch 13/100
10/10 [==============================] - 2s 181ms/step - loss: 0.0601 - val_loss: 0.0391
Epoch 14/100
10/10 [==============================] - 2s 179ms/step - loss: 0.0612 - val_loss: 0.0648
Epoch 15/100
10/10 [==============================] - 2s 182ms/step - loss: 0.0544 - val_loss: 0.1088
Epoch 16/100
10/10 [==============================] - 2s 178ms/step - loss: 0.0548 - val_loss: 0.1088
Epoch 17/100
10/10 [==============================] - 2s 183ms/step - loss: 0.0527 - val_loss: 0.1507
Epoch 18/100
10/10 [==============================] - 2s 177ms/step - loss: 0.0543 - val_loss: 0.0670
Epoch 19/100
10/10 [==============================] - 2s 179ms/step - loss: 0.0514 - val_loss: 0.1228
Epoch 20/100
10/10 [==============================] - 2s 183ms/step - loss: 0.0477 - val_loss: 0.0686
Epoch 21/100
10/10 [==============================] - 2s 174ms/step - loss: 0.0479 - val_loss: 0.0381
Epoch 22/100
10/10 [==============================] - 2s 177ms/step - loss: 0.0458 - val_loss: 0.0494
Epoch 23/100
10/10 [==============================] - 2s 181ms/step - loss: 0.0432 - val_loss: 0.0688
Epoch 24/100
10/10 [==============================] - 2s 172ms/step - loss: 0.0415 - val_loss: 0.0583
Epoch 25/100
10/10 [==============================] - 1s 143ms/step - loss: 0.0404 - val_loss: 0.0856
Epoch 26/100
10/10 [==============================] - 1s 138ms/step - loss: 0.0382 - val_loss: 0.0404
Epoch 27/100
10/10 [==============================] - 1s 144ms/step - loss: 0.0387 - val_loss: 0.0757
Epoch 28/100
10/10 [==============================] - 2s 151ms/step - loss: 0.0374 - val_loss: 0.0608
Epoch 29/100
10/10 [==============================] - 2s 175ms/step - loss: 0.0362 - val_loss: 0.0599
Epoch 30/100
10/10 [==============================] - 2s 161ms/step - loss: 0.0406 - val_loss: 0.1289
Epoch 31/100
10/10 [==============================] - 2s 159ms/step - loss: 0.0426 - val_loss: 0.0708
Epoch 32/100
10/10 [==============================] - 2s 173ms/step - loss: 0.0368 - val_loss: 0.0411
Epoch 33/100
10/10 [==============================] - 2s 151ms/step - loss: 0.0386 - val_loss: 0.0576
Epoch 34/100
10/10 [==============================] - 2s 175ms/step - loss: 0.0341 - val_loss: 0.0503
Epoch 35/100
10/10 [==============================] - 2s 179ms/step - loss: 0.0340 - val_loss: 0.0452
Epoch 36/100
10/10 [==============================] - 2s 177ms/step - loss: 0.0359 - val_loss: 0.0775
Epoch 37/100
10/10 [==============================] - 2s 176ms/step - loss: 0.0354 - val_loss: 0.0784
Epoch 38/100
10/10 [==============================] - 2s 178ms/step - loss: 0.0332 - val_loss: 0.0569
Epoch 39/100
10/10 [==============================] - 2s 183ms/step - loss: 0.0320 - val_loss: 0.0503
Epoch 40/100
10/10 [==============================] - 2s 178ms/step - loss: 0.0318 - val_loss: 0.0617
Epoch 41/100
10/10 [==============================] - 2s 176ms/step - loss: 0.0309 - val_loss: 0.0429
Epoch 42/100
10/10 [==============================] - 2s 177ms/step - loss: 0.0297 - val_loss: 0.0392
Epoch 43/100
10/10 [==============================] - 2s 177ms/step - loss: 0.0332 - val_loss: 0.0374
Epoch 44/100
10/10 [==============================] - 2s 177ms/step - loss: 0.0324 - val_loss: 0.0641
Epoch 45/100
10/10 [==============================] - 2s 179ms/step - loss: 0.0269 - val_loss: 0.0832
Epoch 46/100
10/10 [==============================] - 2s 177ms/step - loss: 0.0279 - val_loss: 0.0368
Epoch 47/100
10/10 [==============================] - 2s 176ms/step - loss: 0.0267 - val_loss: 0.0475
Epoch 48/100
10/10 [==============================] - 2s 179ms/step - loss: 0.0292 - val_loss: 0.0511
Epoch 49/100
10/10 [==============================] - 2s 181ms/step - loss: 0.0278 - val_loss: 0.0362
Epoch 50/100
10/10 [==============================] - 2s 178ms/step - loss: 0.0262 - val_loss: 0.0437
Epoch 51/100
10/10 [==============================] - 2s 178ms/step - loss: 0.0271 - val_loss: 0.0360
Epoch 52/100
10/10 [==============================] - 2s 178ms/step - loss: 0.0236 - val_loss: 0.0596
Epoch 53/100
10/10 [==============================] - 2s 180ms/step - loss: 0.0265 - val_loss: 0.0528
Epoch 54/100
10/10 [==============================] - 2s 166ms/step - loss: 0.0269 - val_loss: 0.0316
Epoch 55/100
10/10 [==============================] - 2s 178ms/step - loss: 0.0281 - val_loss: 0.0444
Epoch 56/100
10/10 [==============================] - 2s 177ms/step - loss: 0.0262 - val_loss: 0.0689
Epoch 57/100
10/10 [==============================] - 2s 174ms/step - loss: 0.0245 - val_loss: 0.0316
Epoch 58/100
10/10 [==============================] - 2s 174ms/step - loss: 0.0262 - val_loss: 0.0273
Epoch 59/100
10/10 [==============================] - 2s 179ms/step - loss: 0.0241 - val_loss: 0.0281
Epoch 60/100
10/10 [==============================] - 2s 182ms/step - loss: 0.0232 - val_loss: 0.0300
Epoch 61/100
10/10 [==============================] - 2s 171ms/step - loss: 0.0217 - val_loss: 0.0393
Epoch 62/100
10/10 [==============================] - 2s 178ms/step - loss: 0.0231 - val_loss: 0.0440
Epoch 63/100
10/10 [==============================] - 2s 173ms/step - loss: 0.0221 - val_loss: 0.0252
Epoch 64/100
10/10 [==============================] - 2s 175ms/step - loss: 0.0242 - val_loss: 0.0242
Epoch 65/100
10/10 [==============================] - 2s 172ms/step - loss: 0.0246 - val_loss: 0.0219
Epoch 66/100
10/10 [==============================] - 2s 180ms/step - loss: 0.0236 - val_loss: 0.0225
Epoch 67/100
10/10 [==============================] - 2s 179ms/step - loss: 0.0219 - val_loss: 0.0222
Epoch 68/100
10/10 [==============================] - 2s 181ms/step - loss: 0.0229 - val_loss: 0.0219
Epoch 69/100
10/10 [==============================] - 2s 174ms/step - loss: 0.0191 - val_loss: 0.0337
Epoch 70/100
10/10 [==============================] - 2s 175ms/step - loss: 0.0213 - val_loss: 0.0488
Epoch 71/100
10/10 [==============================] - 2s 176ms/step - loss: 0.0214 - val_loss: 0.0268
Epoch 72/100
10/10 [==============================] - 2s 171ms/step - loss: 0.0229 - val_loss: 0.0240
Epoch 73/100
10/10 [==============================] - 2s 178ms/step - loss: 0.0239 - val_loss: 0.0384
Epoch 74/100
10/10 [==============================] - 2s 179ms/step - loss: 0.0229 - val_loss: 0.0512
Epoch 75/100
10/10 [==============================] - 2s 180ms/step - loss: 0.0201 - val_loss: 0.0204
Epoch 76/100
10/10 [==============================] - 2s 179ms/step - loss: 0.0207 - val_loss: 0.0382
Epoch 77/100
10/10 [==============================] - 2s 179ms/step - loss: 0.0222 - val_loss: 0.0295
Epoch 78/100
10/10 [==============================] - 2s 178ms/step - loss: 0.0205 - val_loss: 0.0261
Epoch 79/100
10/10 [==============================] - 2s 177ms/step - loss: 0.0196 - val_loss: 0.0569
Epoch 80/100
10/10 [==============================] - 2s 177ms/step - loss: 0.0214 - val_loss: 0.0558
Epoch 81/100
10/10 [==============================] - 2s 177ms/step - loss: 0.0200 - val_loss: 0.0222
Epoch 82/100
10/10 [==============================] - 2s 180ms/step - loss: 0.0194 - val_loss: 0.0168
Epoch 83/100
10/10 [==============================] - 2s 182ms/step - loss: 0.0195 - val_loss: 0.0162
Epoch 84/100
10/10 [==============================] - 2s 179ms/step - loss: 0.0190 - val_loss: 0.0303
Epoch 85/100
10/10 [==============================] - 2s 179ms/step - loss: 0.0193 - val_loss: 0.0153
Epoch 86/100
10/10 [==============================] - 2s 179ms/step - loss: 0.0180 - val_loss: 0.0189
Epoch 87/100
10/10 [==============================] - 2s 180ms/step - loss: 0.0192 - val_loss: 0.0246
Epoch 88/100
10/10 [==============================] - 2s 175ms/step - loss: 0.0174 - val_loss: 0.0129
Epoch 89/100
10/10 [==============================] - 2s 183ms/step - loss: 0.0185 - val_loss: 0.0192
Epoch 90/100
10/10 [==============================] - 2s 181ms/step - loss: 0.0183 - val_loss: 0.0175
Epoch 91/100
10/10 [==============================] - 2s 182ms/step - loss: 0.0175 - val_loss: 0.0154
Epoch 92/100
10/10 [==============================] - 2s 177ms/step - loss: 0.0188 - val_loss: 0.0386
Epoch 93/100
10/10 [==============================] - 2s 180ms/step - loss: 0.0183 - val_loss: 0.0446
Epoch 94/100
10/10 [==============================] - 2s 183ms/step - loss: 0.0180 - val_loss: 0.0154
Epoch 95/100
10/10 [==============================] - 2s 180ms/step - loss: 0.0173 - val_loss: 0.0115
Epoch 96/100
10/10 [==============================] - 2s 179ms/step - loss: 0.0176 - val_loss: 0.0093
Epoch 97/100
10/10 [==============================] - 2s 181ms/step - loss: 0.0164 - val_loss: 0.0085
Epoch 98/100
10/10 [==============================] - 2s 176ms/step - loss: 0.0198 - val_loss: 0.0112
Epoch 99/100
10/10 [==============================] - 2s 176ms/step - loss: 0.0177 - val_loss: 0.0143
Epoch 100/100
10/10 [==============================] - 2s 178ms/step - loss: 0.0171 - val_loss: 0.0213
1/1 [==============================] - 4s 4s/step
        Date          Open
0 2024-05-28  48265.148438
1 2024-05-29  48287.679688
2 2024-05-30  48025.835938
3 2024-05-31  47592.972656
4 2024-06-01  47196.414062
In [16]:
d={'AXIS':df_axis}

for x in d.keys():
    results1(d[x], 30, 1, Scale, x)

Epoch 1/100
9/9 [==============================] - 15s 360ms/step - loss: 1.2815 - val_loss: 2.2663
Epoch 2/100
9/9 [==============================] - 1s 73ms/step - loss: 0.4596 - val_loss: 1.9470
Epoch 3/100
9/9 [==============================] - 1s 73ms/step - loss: 0.2154 - val_loss: 1.8191
Epoch 4/100
9/9 [==============================] - 1s 74ms/step - loss: 0.1963 - val_loss: 1.7838
Epoch 5/100
9/9 [==============================] - 1s 74ms/step - loss: 0.1896 - val_loss: 1.8165
Epoch 6/100
9/9 [==============================] - 1s 72ms/step - loss: 0.2006 - val_loss: 1.8242
Epoch 7/100
9/9 [==============================] - 1s 74ms/step - loss: 0.1728 - val_loss: 1.8098
Epoch 8/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1477 - val_loss: 1.7793
Epoch 9/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1385 - val_loss: 1.7387
Epoch 10/100
9/9 [==============================] - 1s 74ms/step - loss: 0.1270 - val_loss: 1.6867
Epoch 11/100
9/9 [==============================] - 1s 75ms/step - loss: 0.1228 - val_loss: 1.6597
Epoch 12/100
9/9 [==============================] - 1s 74ms/step - loss: 0.1183 - val_loss: 1.5958
Epoch 13/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1090 - val_loss: 1.5575
Epoch 14/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1014 - val_loss: 1.4903
Epoch 15/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0891 - val_loss: 1.4410
Epoch 16/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0974 - val_loss: 1.3638
Epoch 17/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0952 - val_loss: 1.3180
Epoch 18/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0914 - val_loss: 1.2653
Epoch 19/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0780 - val_loss: 1.2678
Epoch 20/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0903 - val_loss: 1.2463
Epoch 21/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0823 - val_loss: 1.1974
Epoch 22/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0824 - val_loss: 1.1620
Epoch 23/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0802 - val_loss: 1.1012
Epoch 24/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0771 - val_loss: 1.0798
Epoch 25/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0736 - val_loss: 1.1385
Epoch 26/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0754 - val_loss: 1.0905
Epoch 27/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0706 - val_loss: 1.0183
Epoch 28/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0537 - val_loss: 0.9801
Epoch 29/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0631 - val_loss: 0.9788
Epoch 30/100
9/9 [==============================] - 1s 77ms/step - loss: 0.0648 - val_loss: 0.9107
Epoch 31/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0669 - val_loss: 0.8480
Epoch 32/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0587 - val_loss: 0.7903
Epoch 33/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0650 - val_loss: 0.7776
Epoch 34/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0588 - val_loss: 0.7830
Epoch 35/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0605 - val_loss: 0.7867
Epoch 36/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0549 - val_loss: 0.7446
Epoch 37/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0593 - val_loss: 0.7240
Epoch 38/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0514 - val_loss: 0.7195
Epoch 39/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0565 - val_loss: 0.7196
Epoch 40/100
9/9 [==============================] - 1s 68ms/step - loss: 0.0572 - val_loss: 0.6691
Epoch 41/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0521 - val_loss: 0.6812
Epoch 42/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0524 - val_loss: 0.6889
Epoch 43/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0473 - val_loss: 0.6566
Epoch 44/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0565 - val_loss: 0.6100
Epoch 45/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0562 - val_loss: 0.5678
Epoch 46/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0484 - val_loss: 0.6110
Epoch 47/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0523 - val_loss: 0.6424
Epoch 48/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0560 - val_loss: 0.6073
Epoch 49/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0463 - val_loss: 0.5832
Epoch 50/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0520 - val_loss: 0.5469
Epoch 51/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0433 - val_loss: 0.5313
Epoch 52/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0468 - val_loss: 0.5521
Epoch 53/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0469 - val_loss: 0.5571
Epoch 54/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0467 - val_loss: 0.5730
Epoch 55/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0441 - val_loss: 0.5759
Epoch 56/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0421 - val_loss: 0.5441
Epoch 57/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0507 - val_loss: 0.5665
Epoch 58/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0449 - val_loss: 0.5340
Epoch 59/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0414 - val_loss: 0.4860
Epoch 60/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0412 - val_loss: 0.4814
Epoch 61/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0427 - val_loss: 0.4704
Epoch 62/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0420 - val_loss: 0.4870
Epoch 63/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0539 - val_loss: 0.4919
Epoch 64/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0399 - val_loss: 0.5101
Epoch 65/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0410 - val_loss: 0.4791
Epoch 66/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0545 - val_loss: 0.4541
Epoch 67/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0471 - val_loss: 0.4764
Epoch 68/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0401 - val_loss: 0.4477
Epoch 69/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0366 - val_loss: 0.4113
Epoch 70/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0399 - val_loss: 0.4572
Epoch 71/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0414 - val_loss: 0.4485
Epoch 72/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0402 - val_loss: 0.4156
Epoch 73/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0396 - val_loss: 0.4292
Epoch 74/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0394 - val_loss: 0.4127
Epoch 75/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0403 - val_loss: 0.3693
Epoch 76/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0352 - val_loss: 0.4177
Epoch 77/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0324 - val_loss: 0.4072
Epoch 78/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0351 - val_loss: 0.3917
Epoch 79/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0338 - val_loss: 0.4292
Epoch 80/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0391 - val_loss: 0.4592
Epoch 81/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0372 - val_loss: 0.4511
Epoch 82/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0346 - val_loss: 0.4456
Epoch 83/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0372 - val_loss: 0.4003
Epoch 84/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0299 - val_loss: 0.3513
Epoch 85/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0331 - val_loss: 0.3679
Epoch 86/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0374 - val_loss: 0.4023
Epoch 87/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0307 - val_loss: 0.4146
Epoch 88/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0324 - val_loss: 0.3948
Epoch 89/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0364 - val_loss: 0.3356
Epoch 90/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0314 - val_loss: 0.3676
Epoch 91/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0346 - val_loss: 0.3588
Epoch 92/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0369 - val_loss: 0.3181
Epoch 93/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0300 - val_loss: 0.3434
Epoch 94/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0318 - val_loss: 0.3626
Epoch 95/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0381 - val_loss: 0.3733
Epoch 96/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0277 - val_loss: 0.3651
Epoch 97/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0335 - val_loss: 0.3362
Epoch 98/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0370 - val_loss: 0.3497
Epoch 99/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0272 - val_loss: 0.3733
Epoch 100/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0333 - val_loss: 0.3553
1/1 [==============================] - 2s 2s/step
        Date         Open
0 2024-05-28  1004.983215
1 2024-05-29  1005.049255
2 2024-05-30  1002.697998
3 2024-05-31  1002.089294
4 2024-06-01  1000.997803
In [17]:
d={'SBI':df_sbi}

for x in d.keys():
    results1(d[x], 30, 1, Scale, x)

Epoch 1/100
9/9 [==============================] - 14s 339ms/step - loss: 1.2947 - val_loss: 2.8727
Epoch 2/100
9/9 [==============================] - 1s 72ms/step - loss: 0.5145 - val_loss: 3.0443
Epoch 3/100
9/9 [==============================] - 1s 71ms/step - loss: 0.3221 - val_loss: 3.1424
Epoch 4/100
9/9 [==============================] - 1s 71ms/step - loss: 0.2079 - val_loss: 3.1975
Epoch 5/100
9/9 [==============================] - 1s 70ms/step - loss: 0.2413 - val_loss: 3.0896
Epoch 6/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1578 - val_loss: 2.9990
Epoch 7/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1414 - val_loss: 2.9790
Epoch 8/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1289 - val_loss: 2.9810
Epoch 9/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1139 - val_loss: 2.9420
Epoch 10/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1120 - val_loss: 2.9086
Epoch 11/100
9/9 [==============================] - 1s 70ms/step - loss: 0.1089 - val_loss: 2.8686
Epoch 12/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0981 - val_loss: 2.8497
Epoch 13/100
9/9 [==============================] - 1s 69ms/step - loss: 0.1080 - val_loss: 2.8398
Epoch 14/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0899 - val_loss: 2.7463
Epoch 15/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0781 - val_loss: 2.7102
Epoch 16/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0831 - val_loss: 2.7137
Epoch 17/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0752 - val_loss: 2.7494
Epoch 18/100
9/9 [==============================] - 1s 76ms/step - loss: 0.0751 - val_loss: 2.6849
Epoch 19/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0719 - val_loss: 2.5862
Epoch 20/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0732 - val_loss: 2.5798
Epoch 21/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0712 - val_loss: 2.5402
Epoch 22/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0657 - val_loss: 2.3843
Epoch 23/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0572 - val_loss: 2.4614
Epoch 24/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0615 - val_loss: 2.4154
Epoch 25/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0549 - val_loss: 2.4718
Epoch 26/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0529 - val_loss: 2.4202
Epoch 27/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0518 - val_loss: 2.4022
Epoch 28/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0482 - val_loss: 2.3201
Epoch 29/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0439 - val_loss: 2.2988
Epoch 30/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0511 - val_loss: 2.2942
Epoch 31/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0506 - val_loss: 2.3010
Epoch 32/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0416 - val_loss: 2.2107
Epoch 33/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0395 - val_loss: 2.2046
Epoch 34/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0481 - val_loss: 2.2677
Epoch 35/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0482 - val_loss: 2.1916
Epoch 36/100
9/9 [==============================] - 1s 68ms/step - loss: 0.0434 - val_loss: 2.2142
Epoch 37/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0427 - val_loss: 2.1953
Epoch 38/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0366 - val_loss: 2.1868
Epoch 39/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0365 - val_loss: 2.1803
Epoch 40/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0402 - val_loss: 2.1099
Epoch 41/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0375 - val_loss: 2.1610
Epoch 42/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0434 - val_loss: 2.1751
Epoch 43/100
9/9 [==============================] - 1s 76ms/step - loss: 0.0320 - val_loss: 2.1133
Epoch 44/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0332 - val_loss: 2.0407
Epoch 45/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0343 - val_loss: 2.1300
Epoch 46/100
9/9 [==============================] - 1s 67ms/step - loss: 0.0344 - val_loss: 2.1209
Epoch 47/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0323 - val_loss: 1.9896
Epoch 48/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0386 - val_loss: 2.0257
Epoch 49/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0328 - val_loss: 2.1233
Epoch 50/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0321 - val_loss: 2.0249
Epoch 51/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0339 - val_loss: 2.0313
Epoch 52/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0300 - val_loss: 1.9876
Epoch 53/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0319 - val_loss: 2.0120
Epoch 54/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0289 - val_loss: 2.0267
Epoch 55/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0311 - val_loss: 2.0293
Epoch 56/100
9/9 [==============================] - 1s 66ms/step - loss: 0.0323 - val_loss: 1.9957
Epoch 57/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0290 - val_loss: 1.9842
Epoch 58/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0274 - val_loss: 1.8848
Epoch 59/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0300 - val_loss: 1.9149
Epoch 60/100
9/9 [==============================] - 1s 68ms/step - loss: 0.0385 - val_loss: 1.9697
Epoch 61/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0309 - val_loss: 2.0015
Epoch 62/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0321 - val_loss: 1.9190
Epoch 63/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0281 - val_loss: 1.9405
Epoch 64/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0275 - val_loss: 2.0120
Epoch 65/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0295 - val_loss: 1.8573
Epoch 66/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0317 - val_loss: 1.9380
Epoch 67/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0273 - val_loss: 1.9047
Epoch 68/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0291 - val_loss: 2.0705
Epoch 69/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0276 - val_loss: 1.9649
Epoch 70/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0294 - val_loss: 1.8771
Epoch 71/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0354 - val_loss: 2.0045
Epoch 72/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0287 - val_loss: 1.9363
Epoch 73/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0312 - val_loss: 2.0322
Epoch 74/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0255 - val_loss: 1.8806
Epoch 75/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0218 - val_loss: 1.9377
Epoch 76/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0271 - val_loss: 1.8524
Epoch 77/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0237 - val_loss: 1.9184
Epoch 78/100
9/9 [==============================] - 1s 66ms/step - loss: 0.0248 - val_loss: 1.8407
Epoch 79/100
9/9 [==============================] - 1s 66ms/step - loss: 0.0278 - val_loss: 1.8709
Epoch 80/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0278 - val_loss: 1.9255
Epoch 81/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0256 - val_loss: 1.8556
Epoch 82/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0267 - val_loss: 1.8318
Epoch 83/100
9/9 [==============================] - 1s 68ms/step - loss: 0.0263 - val_loss: 1.9535
Epoch 84/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0288 - val_loss: 1.8208
Epoch 85/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0313 - val_loss: 1.8633
Epoch 86/100
9/9 [==============================] - 1s 68ms/step - loss: 0.0252 - val_loss: 1.8127
Epoch 87/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0191 - val_loss: 1.8899
Epoch 88/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0218 - val_loss: 1.8401
Epoch 89/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0297 - val_loss: 1.8699
Epoch 90/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0231 - val_loss: 1.8085
Epoch 91/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0304 - val_loss: 1.8778
Epoch 92/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0230 - val_loss: 1.9085
Epoch 93/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0250 - val_loss: 1.8107
Epoch 94/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0239 - val_loss: 1.8469
Epoch 95/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0266 - val_loss: 1.8633
Epoch 96/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0300 - val_loss: 1.8188
Epoch 97/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0206 - val_loss: 1.7945
Epoch 98/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0250 - val_loss: 1.8483
Epoch 99/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0216 - val_loss: 1.7782
Epoch 100/100
9/9 [==============================] - 1s 68ms/step - loss: 0.0214 - val_loss: 1.8283
1/1 [==============================] - 2s 2s/step
        Date        Open
0 2024-05-28  584.048218
1 2024-05-29  583.750610
2 2024-05-30  583.980347
3 2024-05-31  583.785156
4 2024-06-01  583.517822
In [18]:
d={'PNB': df_pnb}

for x in d.keys():
    results1(d[x], 30, 1, Scale, x)

Epoch 1/100
9/9 [==============================] - 15s 332ms/step - loss: 0.2642 - val_loss: 3.5158
Epoch 2/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1171 - val_loss: 3.5019
Epoch 3/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1039 - val_loss: 3.5511
Epoch 4/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0810 - val_loss: 3.5508
Epoch 5/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0747 - val_loss: 3.5827
Epoch 6/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0649 - val_loss: 3.5639
Epoch 7/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0488 - val_loss: 3.6141
Epoch 8/100
9/9 [==============================] - 1s 68ms/step - loss: 0.0525 - val_loss: 3.6362
Epoch 9/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0461 - val_loss: 3.6203
Epoch 10/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0422 - val_loss: 3.5782
Epoch 11/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0360 - val_loss: 3.5689
Epoch 12/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0418 - val_loss: 3.5425
Epoch 13/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0385 - val_loss: 3.5403
Epoch 14/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0341 - val_loss: 3.5518
Epoch 15/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0339 - val_loss: 3.5501
Epoch 16/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0336 - val_loss: 3.5152
Epoch 17/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0293 - val_loss: 3.5021
Epoch 18/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0268 - val_loss: 3.4155
Epoch 19/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0278 - val_loss: 3.3819
Epoch 20/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0281 - val_loss: 3.3925
Epoch 21/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0250 - val_loss: 3.3264
Epoch 22/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0272 - val_loss: 3.2007
Epoch 23/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0254 - val_loss: 3.1676
Epoch 24/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0275 - val_loss: 3.1158
Epoch 25/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0266 - val_loss: 2.9579
Epoch 26/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0254 - val_loss: 2.9319
Epoch 27/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0202 - val_loss: 2.8630
Epoch 28/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0252 - val_loss: 2.7980
Epoch 29/100
9/9 [==============================] - 1s 68ms/step - loss: 0.0232 - val_loss: 2.8192
Epoch 30/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0256 - val_loss: 2.7665
Epoch 31/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0215 - val_loss: 2.6667
Epoch 32/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0255 - val_loss: 2.5676
Epoch 33/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0203 - val_loss: 2.4674
Epoch 34/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0229 - val_loss: 2.4261
Epoch 35/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0222 - val_loss: 2.3590
Epoch 36/100
9/9 [==============================] - 1s 68ms/step - loss: 0.0205 - val_loss: 2.3315
Epoch 37/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0169 - val_loss: 2.2697
Epoch 38/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0184 - val_loss: 2.2963
Epoch 39/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0189 - val_loss: 2.2504
Epoch 40/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0207 - val_loss: 2.2089
Epoch 41/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0173 - val_loss: 2.2072
Epoch 42/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0171 - val_loss: 2.1118
Epoch 43/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0150 - val_loss: 2.0778
Epoch 44/100
9/9 [==============================] - 1s 67ms/step - loss: 0.0182 - val_loss: 2.1587
Epoch 45/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0161 - val_loss: 2.1382
Epoch 46/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0188 - val_loss: 2.0538
Epoch 47/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0171 - val_loss: 2.0019
Epoch 48/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0149 - val_loss: 2.0079
Epoch 49/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0172 - val_loss: 2.0081
Epoch 50/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0171 - val_loss: 1.9715
Epoch 51/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0161 - val_loss: 1.9420
Epoch 52/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0148 - val_loss: 1.9160
Epoch 53/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0154 - val_loss: 1.8934
Epoch 54/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0148 - val_loss: 1.8750
Epoch 55/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0143 - val_loss: 1.9218
Epoch 56/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0156 - val_loss: 1.9584
Epoch 57/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0147 - val_loss: 1.9326
Epoch 58/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0143 - val_loss: 1.9191
Epoch 59/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0178 - val_loss: 1.9138
Epoch 60/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0138 - val_loss: 1.9281
Epoch 61/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0161 - val_loss: 1.8760
Epoch 62/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0156 - val_loss: 1.8147
Epoch 63/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0127 - val_loss: 1.8395
Epoch 64/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0166 - val_loss: 1.8904
Epoch 65/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0140 - val_loss: 1.8610
Epoch 66/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0111 - val_loss: 1.8408
Epoch 67/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0171 - val_loss: 1.8666
Epoch 68/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0132 - val_loss: 1.8593
Epoch 69/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0167 - val_loss: 1.8434
Epoch 70/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0134 - val_loss: 1.7978
Epoch 71/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0119 - val_loss: 1.7948
Epoch 72/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0130 - val_loss: 1.8347
Epoch 73/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0124 - val_loss: 1.8288
Epoch 74/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0123 - val_loss: 1.8746
Epoch 75/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0124 - val_loss: 1.8540
Epoch 76/100
9/9 [==============================] - 1s 76ms/step - loss: 0.0143 - val_loss: 1.8388
Epoch 77/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0139 - val_loss: 1.8485
Epoch 78/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0138 - val_loss: 1.8517
Epoch 79/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0133 - val_loss: 1.8599
Epoch 80/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0098 - val_loss: 1.9397
Epoch 81/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0112 - val_loss: 1.9524
Epoch 82/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0103 - val_loss: 1.8838
Epoch 83/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0123 - val_loss: 1.8342
Epoch 84/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0098 - val_loss: 1.8660
Epoch 85/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0105 - val_loss: 1.9073
Epoch 86/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0115 - val_loss: 1.9099
Epoch 87/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0090 - val_loss: 1.9593
Epoch 88/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0111 - val_loss: 1.9767
Epoch 89/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0108 - val_loss: 2.0461
Epoch 90/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0108 - val_loss: 2.0336
Epoch 91/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0105 - val_loss: 1.9890
Epoch 92/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0103 - val_loss: 1.9419
Epoch 93/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0097 - val_loss: 1.9453
Epoch 94/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0101 - val_loss: 2.0235
Epoch 95/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0096 - val_loss: 1.9700
Epoch 96/100
9/9 [==============================] - 1s 68ms/step - loss: 0.0104 - val_loss: 1.9572
Epoch 97/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0113 - val_loss: 1.9204
Epoch 98/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0109 - val_loss: 1.8773
Epoch 99/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0079 - val_loss: 1.9084
Epoch 100/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0104 - val_loss: 1.9523
1/1 [==============================] - 2s 2s/step
        Date       Open
0 2024-05-28  74.245293
1 2024-05-29  74.150322
2 2024-05-30  74.244240
3 2024-05-31  74.194344
4 2024-06-01  74.108452
In [19]:
d={'KOTAK': df_kot}

for x in d.keys():
    results1(d[x], 30, 1, Scale, x)

Epoch 1/100
9/9 [==============================] - 14s 350ms/step - loss: 0.5819 - val_loss: 0.8997
Epoch 2/100
9/9 [==============================] - 1s 74ms/step - loss: 0.4429 - val_loss: 0.8827
Epoch 3/100
9/9 [==============================] - 1s 74ms/step - loss: 0.3274 - val_loss: 0.8923
Epoch 4/100
9/9 [==============================] - 1s 71ms/step - loss: 0.3001 - val_loss: 0.8981
Epoch 5/100
9/9 [==============================] - 1s 73ms/step - loss: 0.2802 - val_loss: 0.8975
Epoch 6/100
9/9 [==============================] - 1s 73ms/step - loss: 0.2800 - val_loss: 0.9470
Epoch 7/100
9/9 [==============================] - 1s 73ms/step - loss: 0.2202 - val_loss: 0.9717
Epoch 8/100
9/9 [==============================] - 1s 71ms/step - loss: 0.2337 - val_loss: 0.9677
Epoch 9/100
9/9 [==============================] - 1s 71ms/step - loss: 0.2189 - val_loss: 0.9627
Epoch 10/100
9/9 [==============================] - 1s 75ms/step - loss: 0.1909 - val_loss: 0.9466
Epoch 11/100
9/9 [==============================] - 1s 71ms/step - loss: 0.2167 - val_loss: 0.9652
Epoch 12/100
9/9 [==============================] - 1s 73ms/step - loss: 0.2031 - val_loss: 0.9368
Epoch 13/100
9/9 [==============================] - 1s 71ms/step - loss: 0.2029 - val_loss: 0.9636
Epoch 14/100
9/9 [==============================] - 1s 74ms/step - loss: 0.1677 - val_loss: 0.9484
Epoch 15/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1830 - val_loss: 0.9161
Epoch 16/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1549 - val_loss: 0.8759
Epoch 17/100
9/9 [==============================] - 1s 70ms/step - loss: 0.1685 - val_loss: 0.8631
Epoch 18/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1619 - val_loss: 0.7980
Epoch 19/100
9/9 [==============================] - 1s 69ms/step - loss: 0.1671 - val_loss: 0.7994
Epoch 20/100
9/9 [==============================] - 1s 69ms/step - loss: 0.1706 - val_loss: 0.8539
Epoch 21/100
9/9 [==============================] - 1s 70ms/step - loss: 0.1443 - val_loss: 0.8607
Epoch 22/100
9/9 [==============================] - 1s 70ms/step - loss: 0.1570 - val_loss: 0.7795
Epoch 23/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1525 - val_loss: 0.7627
Epoch 24/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1418 - val_loss: 0.7641
Epoch 25/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1534 - val_loss: 0.6861
Epoch 26/100
9/9 [==============================] - 1s 69ms/step - loss: 0.1457 - val_loss: 0.6398
Epoch 27/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1308 - val_loss: 0.6657
Epoch 28/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1660 - val_loss: 0.6169
Epoch 29/100
9/9 [==============================] - 1s 70ms/step - loss: 0.1340 - val_loss: 0.5705
Epoch 30/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1272 - val_loss: 0.5105
Epoch 31/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1150 - val_loss: 0.4859
Epoch 32/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1102 - val_loss: 0.4922
Epoch 33/100
9/9 [==============================] - 1s 76ms/step - loss: 0.1316 - val_loss: 0.4266
Epoch 34/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1524 - val_loss: 0.4250
Epoch 35/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1202 - val_loss: 0.4204
Epoch 36/100
9/9 [==============================] - 1s 76ms/step - loss: 0.1236 - val_loss: 0.3608
Epoch 37/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1214 - val_loss: 0.4056
Epoch 38/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1208 - val_loss: 0.3831
Epoch 39/100
9/9 [==============================] - 1s 70ms/step - loss: 0.1278 - val_loss: 0.4218
Epoch 40/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1370 - val_loss: 0.3689
Epoch 41/100
9/9 [==============================] - 1s 70ms/step - loss: 0.1055 - val_loss: 0.3367
Epoch 42/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1190 - val_loss: 0.3321
Epoch 43/100
9/9 [==============================] - 1s 69ms/step - loss: 0.1141 - val_loss: 0.3188
Epoch 44/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1032 - val_loss: 0.3001
Epoch 45/100
9/9 [==============================] - 1s 70ms/step - loss: 0.1281 - val_loss: 0.3234
Epoch 46/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1039 - val_loss: 0.2798
Epoch 47/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1031 - val_loss: 0.2913
Epoch 48/100
9/9 [==============================] - 1s 70ms/step - loss: 0.1136 - val_loss: 0.2786
Epoch 49/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1074 - val_loss: 0.2494
Epoch 50/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1061 - val_loss: 0.2590
Epoch 51/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1061 - val_loss: 0.2669
Epoch 52/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1155 - val_loss: 0.2709
Epoch 53/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1262 - val_loss: 0.2256
Epoch 54/100
9/9 [==============================] - 1s 69ms/step - loss: 0.1240 - val_loss: 0.2195
Epoch 55/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1050 - val_loss: 0.2082
Epoch 56/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1175 - val_loss: 0.2421
Epoch 57/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0965 - val_loss: 0.2347
Epoch 58/100
9/9 [==============================] - 1s 76ms/step - loss: 0.1077 - val_loss: 0.2215
Epoch 59/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0959 - val_loss: 0.2083
Epoch 60/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1056 - val_loss: 0.2208
Epoch 61/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1058 - val_loss: 0.2150
Epoch 62/100
9/9 [==============================] - 1s 70ms/step - loss: 0.1006 - val_loss: 0.2078
Epoch 63/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0885 - val_loss: 0.2023
Epoch 64/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0997 - val_loss: 0.1927
Epoch 65/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1041 - val_loss: 0.1912
Epoch 66/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0930 - val_loss: 0.2056
Epoch 67/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0955 - val_loss: 0.2019
Epoch 68/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0894 - val_loss: 0.1954
Epoch 69/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0934 - val_loss: 0.1791
Epoch 70/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0910 - val_loss: 0.1946
Epoch 71/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1046 - val_loss: 0.1957
Epoch 72/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1025 - val_loss: 0.1699
Epoch 73/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0898 - val_loss: 0.1810
Epoch 74/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0929 - val_loss: 0.1643
Epoch 75/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0760 - val_loss: 0.1901
Epoch 76/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0898 - val_loss: 0.1680
Epoch 77/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0932 - val_loss: 0.1718
Epoch 78/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0909 - val_loss: 0.1950
Epoch 79/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0862 - val_loss: 0.1888
Epoch 80/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0728 - val_loss: 0.1916
Epoch 81/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0840 - val_loss: 0.1531
Epoch 82/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1001 - val_loss: 0.1735
Epoch 83/100
9/9 [==============================] - 1s 68ms/step - loss: 0.0824 - val_loss: 0.1536
Epoch 84/100
9/9 [==============================] - 1s 74ms/step - loss: 0.1009 - val_loss: 0.1584
Epoch 85/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0884 - val_loss: 0.1568
Epoch 86/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0957 - val_loss: 0.1513
Epoch 87/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0925 - val_loss: 0.1550
Epoch 88/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0839 - val_loss: 0.1458
Epoch 89/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0902 - val_loss: 0.1484
Epoch 90/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0915 - val_loss: 0.1359
Epoch 91/100
9/9 [==============================] - 1s 68ms/step - loss: 0.0800 - val_loss: 0.1379
Epoch 92/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0806 - val_loss: 0.1435
Epoch 93/100
9/9 [==============================] - 1s 69ms/step - loss: 0.0962 - val_loss: 0.1508
Epoch 94/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0808 - val_loss: 0.1519
Epoch 95/100
9/9 [==============================] - 1s 76ms/step - loss: 0.0956 - val_loss: 0.1554
WARNING:tensorflow:5 out of the last 5 calls to <function Model.make_predict_function.<locals>.predict_function at 0x00000251F9220C10> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
1/1 [==============================] - 2s 2s/step
        Date         Open
0 2024-05-28  1796.508423
1 2024-05-29  1797.779541
2 2024-05-30  1794.931152
3 2024-05-31  1792.695801
4 2024-06-01  1791.800171
In [20]:
d={ 'ICIC': df_icic}

for x in d.keys():
    results1(d[x], 30, 1, Scale, x)

Epoch 1/100
9/9 [==============================] - 15s 318ms/step - loss: 0.3882 - val_loss: 2.1026
Epoch 2/100
9/9 [==============================] - 1s 75ms/step - loss: 0.1982 - val_loss: 1.9187
Epoch 3/100
9/9 [==============================] - 1s 77ms/step - loss: 0.1473 - val_loss: 1.8974
Epoch 4/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1312 - val_loss: 1.8731
Epoch 5/100
9/9 [==============================] - 1s 75ms/step - loss: 0.1128 - val_loss: 1.8461
Epoch 6/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1031 - val_loss: 1.8503
Epoch 7/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0932 - val_loss: 1.8140
Epoch 8/100
9/9 [==============================] - 1s 77ms/step - loss: 0.0889 - val_loss: 1.7716
Epoch 9/100
9/9 [==============================] - 1s 77ms/step - loss: 0.0823 - val_loss: 1.7217
Epoch 10/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0690 - val_loss: 1.6316
Epoch 11/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0705 - val_loss: 1.5702
Epoch 12/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0708 - val_loss: 1.5328
Epoch 13/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0746 - val_loss: 1.5134
Epoch 14/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0569 - val_loss: 1.4483
Epoch 15/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0604 - val_loss: 1.3979
Epoch 16/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0583 - val_loss: 1.3730
Epoch 17/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0588 - val_loss: 1.3202
Epoch 18/100
9/9 [==============================] - 1s 77ms/step - loss: 0.0512 - val_loss: 1.2529
Epoch 19/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0525 - val_loss: 1.2134
Epoch 20/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0601 - val_loss: 1.1579
Epoch 21/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0519 - val_loss: 1.1100
Epoch 22/100
9/9 [==============================] - 1s 76ms/step - loss: 0.0496 - val_loss: 1.0810
Epoch 23/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0554 - val_loss: 1.0279
Epoch 24/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0515 - val_loss: 1.0095
Epoch 25/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0421 - val_loss: 0.9724
Epoch 26/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0478 - val_loss: 0.9405
Epoch 27/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0430 - val_loss: 0.9144
Epoch 28/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0387 - val_loss: 0.8834
Epoch 29/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0446 - val_loss: 0.8670
Epoch 30/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0484 - val_loss: 0.8250
Epoch 31/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0544 - val_loss: 0.7721
Epoch 32/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0371 - val_loss: 0.7576
Epoch 33/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0411 - val_loss: 0.7506
Epoch 34/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0407 - val_loss: 0.7001
Epoch 35/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0484 - val_loss: 0.6631
Epoch 36/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0410 - val_loss: 0.6700
Epoch 37/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0394 - val_loss: 0.6532
Epoch 38/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0408 - val_loss: 0.6336
Epoch 39/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0470 - val_loss: 0.6286
Epoch 40/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0408 - val_loss: 0.6333
Epoch 41/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0360 - val_loss: 0.6082
Epoch 42/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0359 - val_loss: 0.5663
Epoch 43/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0455 - val_loss: 0.5433
Epoch 44/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0398 - val_loss: 0.5529
Epoch 45/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0394 - val_loss: 0.5720
Epoch 46/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0380 - val_loss: 0.5783
Epoch 47/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0342 - val_loss: 0.5428
Epoch 48/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0355 - val_loss: 0.5206
Epoch 49/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0395 - val_loss: 0.5307
Epoch 50/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0406 - val_loss: 0.5289
Epoch 51/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0342 - val_loss: 0.4877
Epoch 52/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0414 - val_loss: 0.4784
Epoch 53/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0278 - val_loss: 0.4918
Epoch 54/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0344 - val_loss: 0.4976
Epoch 55/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0348 - val_loss: 0.4964
Epoch 56/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0293 - val_loss: 0.4964
Epoch 57/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0294 - val_loss: 0.4823
Epoch 58/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0416 - val_loss: 0.4329
Epoch 59/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0278 - val_loss: 0.3990
Epoch 60/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0271 - val_loss: 0.4032
Epoch 61/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0319 - val_loss: 0.4498
Epoch 62/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0487 - val_loss: 0.4912
Epoch 63/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0286 - val_loss: 0.4560
Epoch 64/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0314 - val_loss: 0.4172
Epoch 65/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0339 - val_loss: 0.4107
Epoch 66/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0316 - val_loss: 0.4202
Epoch 67/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0239 - val_loss: 0.4071
Epoch 68/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0311 - val_loss: 0.4000
Epoch 69/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0300 - val_loss: 0.4120
Epoch 70/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0280 - val_loss: 0.4277
Epoch 71/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0444 - val_loss: 0.4204
Epoch 72/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0257 - val_loss: 0.3902
Epoch 73/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0287 - val_loss: 0.3708
Epoch 74/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0356 - val_loss: 0.3782
Epoch 75/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0298 - val_loss: 0.3696
Epoch 76/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0393 - val_loss: 0.3619
Epoch 77/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0258 - val_loss: 0.3881
Epoch 78/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0373 - val_loss: 0.3967
Epoch 79/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0292 - val_loss: 0.3761
Epoch 80/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0330 - val_loss: 0.3741
Epoch 81/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0266 - val_loss: 0.3862
Epoch 82/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0365 - val_loss: 0.3882
WARNING:tensorflow:6 out of the last 6 calls to <function Model.make_predict_function.<locals>.predict_function at 0x00000251E5834790> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.
1/1 [==============================] - 2s 2s/step
        Date        Open
0 2024-05-28  980.136047
1 2024-05-29  978.922119
2 2024-05-30  978.279114
3 2024-05-31  975.935364
4 2024-06-01  973.810364
In [21]:
d={'INDUSIND':df_ind}

for x in d.keys():
    results1(d[x], 30, 1, Scale, x)

Epoch 1/100
9/9 [==============================] - 14s 334ms/step - loss: 0.4733 - val_loss: 1.8298
Epoch 2/100
9/9 [==============================] - 1s 73ms/step - loss: 0.2766 - val_loss: 1.7224
Epoch 3/100
9/9 [==============================] - 1s 75ms/step - loss: 0.2139 - val_loss: 1.7562
Epoch 4/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1777 - val_loss: 1.7402
Epoch 5/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1518 - val_loss: 1.6177
Epoch 6/100
9/9 [==============================] - 1s 71ms/step - loss: 0.1451 - val_loss: 1.5785
Epoch 7/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1346 - val_loss: 1.5411
Epoch 8/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1212 - val_loss: 1.4331
Epoch 9/100
9/9 [==============================] - 1s 75ms/step - loss: 0.1207 - val_loss: 1.3920
Epoch 10/100
9/9 [==============================] - 1s 76ms/step - loss: 0.1246 - val_loss: 1.3922
Epoch 11/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1190 - val_loss: 1.3410
Epoch 12/100
9/9 [==============================] - 1s 73ms/step - loss: 0.1011 - val_loss: 1.2924
Epoch 13/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0940 - val_loss: 1.2709
Epoch 14/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0840 - val_loss: 1.1893
Epoch 15/100
9/9 [==============================] - 1s 72ms/step - loss: 0.1025 - val_loss: 1.1250
Epoch 16/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0832 - val_loss: 1.1229
Epoch 17/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0743 - val_loss: 1.0927
Epoch 18/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0773 - val_loss: 1.0375
Epoch 19/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0774 - val_loss: 0.9770
Epoch 20/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0629 - val_loss: 0.9230
Epoch 21/100
9/9 [==============================] - 1s 77ms/step - loss: 0.0611 - val_loss: 0.9039
Epoch 22/100
9/9 [==============================] - 1s 63ms/step - loss: 0.0605 - val_loss: 0.8280
Epoch 23/100
9/9 [==============================] - 1s 64ms/step - loss: 0.0773 - val_loss: 0.7488
Epoch 24/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0668 - val_loss: 0.6384
Epoch 25/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0646 - val_loss: 0.6163
Epoch 26/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0544 - val_loss: 0.6996
Epoch 27/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0546 - val_loss: 0.5934
Epoch 28/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0666 - val_loss: 0.5273
Epoch 29/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0599 - val_loss: 0.4752
Epoch 30/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0544 - val_loss: 0.4113
Epoch 31/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0518 - val_loss: 0.4278
Epoch 32/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0626 - val_loss: 0.4239
Epoch 33/100
9/9 [==============================] - 1s 68ms/step - loss: 0.0488 - val_loss: 0.3451
Epoch 34/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0521 - val_loss: 0.3343
Epoch 35/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0563 - val_loss: 0.3236
Epoch 36/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0487 - val_loss: 0.3474
Epoch 37/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0440 - val_loss: 0.3153
Epoch 38/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0550 - val_loss: 0.3198
Epoch 39/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0488 - val_loss: 0.2955
Epoch 40/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0477 - val_loss: 0.2552
Epoch 41/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0463 - val_loss: 0.2747
Epoch 42/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0474 - val_loss: 0.3007
Epoch 43/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0462 - val_loss: 0.2455
Epoch 44/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0516 - val_loss: 0.2398
Epoch 45/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0467 - val_loss: 0.2201
Epoch 46/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0426 - val_loss: 0.2191
Epoch 47/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0459 - val_loss: 0.2176
Epoch 48/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0406 - val_loss: 0.2664
Epoch 49/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0339 - val_loss: 0.3001
Epoch 50/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0476 - val_loss: 0.2799
Epoch 51/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0443 - val_loss: 0.2690
Epoch 52/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0384 - val_loss: 0.2423
Epoch 53/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0456 - val_loss: 0.2003
Epoch 54/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0367 - val_loss: 0.2381
Epoch 55/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0408 - val_loss: 0.2308
Epoch 56/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0395 - val_loss: 0.2050
Epoch 57/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0408 - val_loss: 0.2198
Epoch 58/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0372 - val_loss: 0.2412
Epoch 59/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0337 - val_loss: 0.2344
Epoch 60/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0392 - val_loss: 0.2393
Epoch 61/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0428 - val_loss: 0.2155
Epoch 62/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0389 - val_loss: 0.2112
Epoch 63/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0387 - val_loss: 0.1990
Epoch 64/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0436 - val_loss: 0.1879
Epoch 65/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0306 - val_loss: 0.2109
Epoch 66/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0323 - val_loss: 0.2663
Epoch 67/100
9/9 [==============================] - 1s 75ms/step - loss: 0.0383 - val_loss: 0.2780
Epoch 68/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0410 - val_loss: 0.2537
Epoch 69/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0386 - val_loss: 0.2144
Epoch 70/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0316 - val_loss: 0.2147
Epoch 71/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0345 - val_loss: 0.2236
Epoch 72/100
9/9 [==============================] - 1s 68ms/step - loss: 0.0309 - val_loss: 0.2302
Epoch 73/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0323 - val_loss: 0.2042
Epoch 74/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0359 - val_loss: 0.2122
Epoch 75/100
9/9 [==============================] - 1s 76ms/step - loss: 0.0336 - val_loss: 0.2710
Epoch 76/100
9/9 [==============================] - 1s 59ms/step - loss: 0.0310 - val_loss: 0.2625
Epoch 77/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0331 - val_loss: 0.2532
Epoch 78/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0366 - val_loss: 0.2428
Epoch 79/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0305 - val_loss: 0.2063
Epoch 80/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0352 - val_loss: 0.2275
Epoch 81/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0352 - val_loss: 0.2262
Epoch 82/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0309 - val_loss: 0.1843
Epoch 83/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0349 - val_loss: 0.1970
Epoch 84/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0280 - val_loss: 0.1909
Epoch 85/100
9/9 [==============================] - 1s 70ms/step - loss: 0.0292 - val_loss: 0.2449
Epoch 86/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0283 - val_loss: 0.2619
Epoch 87/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0310 - val_loss: 0.2491
Epoch 88/100
9/9 [==============================] - 1s 76ms/step - loss: 0.0302 - val_loss: 0.2358
Epoch 89/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0323 - val_loss: 0.2497
Epoch 90/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0365 - val_loss: 0.2352
Epoch 91/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0321 - val_loss: 0.1962
Epoch 92/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0345 - val_loss: 0.1872
Epoch 93/100
9/9 [==============================] - 1s 71ms/step - loss: 0.0366 - val_loss: 0.2025
Epoch 94/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0287 - val_loss: 0.2419
Epoch 95/100
9/9 [==============================] - 1s 74ms/step - loss: 0.0286 - val_loss: 0.2534
Epoch 96/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0325 - val_loss: 0.1876
Epoch 97/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0285 - val_loss: 0.1858
Epoch 98/100
9/9 [==============================] - 1s 73ms/step - loss: 0.0276 - val_loss: 0.1968
Epoch 99/100
9/9 [==============================] - 1s 67ms/step - loss: 0.0306 - val_loss: 0.2079
Epoch 100/100
9/9 [==============================] - 1s 72ms/step - loss: 0.0236 - val_loss: 0.2255
1/1 [==============================] - 2s 2s/step
        Date         Open
0 2024-05-28  1421.441772
1 2024-05-29  1418.887817
2 2024-05-30  1416.919312
3 2024-05-31  1421.852539
4 2024-06-01  1425.205688
In [ ]: